Methods for Georeferencing Linear and Non-Linear Media Content

Horbach, Flavio; Visca, Dominik; Pagel, Sven; Neis, Pascal

doi:10.1553/giscience2022_02_s66

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…However, as technologies advanced, these methods saw reduced adoption, overshadowed by metadata-centric strategies, particularly within social media realms in which user-generated content provides a plethora of metadata ripe for geocod-ing [24,25]. Based on the same approach, Horbach et al (2022) [26] also focus on mere metadata analysis but show the basic usability of image and audio analysis.…”

Section: Related Workmentioning

confidence: 99%

Advanced Techniques for Geospatial Referencing in Online Media Repositories

Warch,

Stellbauer,

Neis

2024

Future Internet

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In the digital transformation era, video media libraries’ untapped potential is immense, restricted primarily by their non-machine-readable nature and basic search functionalities limited to standard metadata. This study presents a novel multimodal methodology that utilizes advances in artificial intelligence, including neural networks, computer vision, and natural language processing, to extract and geocode geospatial references from videos. Leveraging the geospatial information from videos enables semantic searches, enhances search relevance, and allows for targeted advertising, particularly on mobile platforms. The methodology involves a comprehensive process, including data acquisition from ARD Mediathek, image and text analysis using advanced machine learning models, and audio and subtitle processing with state-of-the-art linguistic models. Despite challenges like model interpretability and the complexity of geospatial data extraction, this study’s findings indicate significant potential for advancing the precision of spatial data analysis within video content, promising to enrich media libraries with more navigable, contextually rich content. This advancement has implications for user engagement, targeted services, and broader urban planning and cultural heritage applications.

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Section: Related Workmentioning

confidence: 99%